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Current technology makes it easy for humans to take an image and convert it to digital content, but sometimes there is additional noise in the image so it looks damaged. The damage that often occurs, like blurring and excessive noise in digital images, can certainly affect the meaning and quality of the image. Image restoration is a process used to restore the image to its original state before the image damage occurs. In this research, we proposed an image restoration method by combining Wavelet transformation and Akamatsu transformation. Based on previous research Akamatsu's transformation only works well on blurred images. In order not to focus solely on blurry images, Akamatsu's transformation will be applied based on Wavelet transformations on high-low (HL), low-high (LH), and high-high (HH) subunits. The result of the proposed method will be comparable with the previous methods. PSNR is used as a measure of image quality restoration. Based on the results the proposed method can improve the quality of the restoration on image noise, such as Gaussian, salt and pepper, and also works well on blurred images. The average increase is around 2 dB based on the PSNR calculation.

The mathematical and theoretical development required for using the minimum mutual information technique in image restoration is presented. Results from an implementation of the minimum mutual information technique are shown to demonstrate its capabilities. The results establish that minimum mutual information is a viable image restoration approach without the convergence problems usually associated with similar techniques.

The cybernetic approach differs significantly from the conventional reductionist methods of natural and biological sciences. Norbert Wiener established the theory of cybernetics as a science of control and communication process in living beings (human and animals) and machines. Dutta Majumder in his Norbert Wiener Award winning paper extended the approach to include integrated complex human machine systems and functions with general systems theory as a unitary science laying the mathematical foundation for unifying observing systems, observed systems and the act of observing as indicated in von Foerster’s concept of second‐order cybernetics. Both from the point of view of ontology and that of epistemology the cybernetic approach now enables computer technology to incorporate artificial intelligence (AI) and expert system (ES) for knowledge based instrumentation for diagnostics and therapy planning. Presents the results of a project for development of a knowledge based framework for combining different modalities of medical image processing such as CT, MR(T₁), MR(T₂), SPECT, PET, USG etc. whichever is relevant for particular pathological investigation for diagnostics and therapeutic planning. Experiments were conducted with (a) Alzheimer’s patient data and (b) detection and grading of malignancy with oncological data for the cancer screening system.

Developing an effective destination branding strategy in places that have a negative image is still a difficult challenge to overcome for place marketers. Therefore, the purpose of this paper is to analyze how developing and promoting a new cuisine can transform a negative destination image into a positive one. The main premise is that food can be an effective way of rebranding a destination.

The research method used for this exploratory study was the qualitative content analysis of 50 articles from newspapers and magazines in English and websites from the Mexican tourism boards websites.

The results of the qualitative content analysis showed that local chefs (who are proud of their city and aim to promote their cuisine through the use of local products and the organization of food events) are fundamental for the development of a new and successful cuisine able to change the negative image of a destination.

The paper provides an innovative approach to destination image restoration by analyzing local gastronomy as an important means to offset the negative media projection of a damaged destination image.

The purpose of this paper is to explore the ways in which a leading Australian public company uses sustainability reporting to respond to reputation risk arising from proposed regulation.

The paper uses a case study approach and both qualitative and quantitative methods of content analysis. The qualitative component is based on a framework of reputation conceptualisations and image restoration strategies adopted from existing literature.

The key findings of this paper are that the concept of reputation risk management (RRM) could assist in understanding what motivates sustainability reporting, and how proposed regulation could lead to a decrease in the quantity but increase in the quality of sustainability reporting. In addition, “honesty” is revealed as a potential RRM strategy.

The paper extends existing research on the RRM thesis by studying an Australian case of a reputation‐damaging event over a number of reporting years, examining a range of sustainability reporting media, and adding a quantitative aspect to an otherwise qualitative research framework.

This study aims to explore the use of corporate social responsibility (CSR) disclosures by the “Big Four” Australian banks post the banking royal commission (BRC) to manage their reputational risk.

This paper uses a case study approach through a thematic analysis of the Big Four banks’ annual and sustainability reports and uses reputation risk management (RRM) as a conceptual lens to explore the image restoration strategies used by these banks.

The study finds that a corrective action strategy was disclosed extensively by all four banks whereby each bank outlined the actions that they were undertaking to correct the deficiencies identified by the BRC. However, the impact of these proposed actions was tampered by the fact that each bank sought to use strategies to reduce the offensiveness of their misdemeanours. It is argued that while disclosure on corrective actions and compensation is useful, an emphasis on reducing offensiveness of actions impacts the effectiveness of banks’ responses and their acceptance of full responsibility for their actions.

This paper applies the RRM perspective to a recent reputation damaging event, thereby expanding the literature on image restoration strategies used by companies during major incidents.

This study provides useful insights in relation to the approaches used to manage the reputational risk arising from the BRC. It provides insights into the credibility of information disclosed post an incident and has potential implications for the assurance of such information.

Given the critical importance of the banking industry to modern society, misconduct in this sector needs a closer examination, requiring a greater need for responsibility from its key players.

This study extends the applicability of the RRM perspective to a social incident and highlights that it is reputation, rather than legitimacy, that is critical when organisations in an industry face extensive public scrutiny. A thematic analysis approach adds value to the methods used for analysing CSR disclosures.

The purpose of this paper is to study whether the controversy because of brand crisis based on quality had any impact on consumer brand equity, brand image, brand reliability, brand perception of quality, perceived value, brand sentiments and purchase behavior.

The research methodology consisted of two types of data: primary and secondary data. The secondary research consisted of social media brand sentiments and financial analysis. The primary research focused on perception study of brand quality, consumer brand equity, brand reliability, brand image, purchase and brand switch behavior.

Maggi used social media extensively to address the issue and re-build the brand reliability and confidence among its users. Communication strategy adopted by focusing on the past experience of consumers and using them as a spokesperson generated a positive sentiment towards the brand under crisis.

While Maggi suffered the backlash because of the controversy across the length and breadth of India and was banned in a number of states, the author could conduct the primary research only in one city of the state of Maharashtra, Mumbai. The effectiveness of the survey was impacted because of the geographic limitations the author faced while collecting the responses. The survey would have definitely been more effective, with responses collected from different states and with more number of respondents. Fishbein is very old, from the 1980s, even though this theory has met the test of time. Application of the effect of experiences on experiential perceptions and how this influences value through networking could have been used to explain the same.

An important implication of this paper’s findings for practice, therefore, is that brands should incessantly strive to maintain the consumers’ level of trust, as it is essential for the preservation of the brand equity after a crisis. Crisis-stricken brands should safeguard their reputations from the negative effects of crises. It is even more important for any brand to act appropriately when the cause of the crisis is attributed to its actions and processes. Managers have to address the quality of products in case of brand crisis for restoring trust, image and reliability in the brand. Right type of communication to right targeted consumers will help in the restoration of the image, trust on the brand and bring back loyal customers. Managers have to build brand equity on a regular basis, as a strong brand can recover faster as seen from this paper.

This paper helps to upgrade the knowledge and understanding of the impact of the controversy on brand equity and image and how the crisis management strategy can be adopted to regain the mind share and equity. This paper will help the brands in the future to know how a crisis can be managed efficiently by drawing a cue from the strategies implemented by Maggi.

The purpose of this paper is to introduce an innovative procedure for digital historical documents image binarization based on image pre‐processing and image condition classification. The estimated results for each class of images and each method have shown improved image quality for the six categories of document images described by their separate characteristics.

The applied technique consists of five stages, i.e. text image acquisition, image preparation, denoising, image type classification in six categories according to image condition, image thresholding and final refinement, a very effective approach to binarize document images. The results achieved by the authors' method require minimal pre‐processing steps for best quality of the image and increased text readability. This methodology performs better compared to current state‐of‐the‐art adaptive thresholding techniques.

An innovative procedure for digital historical documents image binarization based on image pre‐processing, image type classification in categories according to image condition and further enhancement. This methodology is robust and simple, with minimal pre‐processing steps for best quality of the image, increased text readability and it performs better compared to available thresholding techniques.

The technique consists of limited but optimized pre‐processing sequential steps, and attention should be given in document image preparation and denoising, and on image condition classification for thresholding and refinement, since bad results in a single stage corrupt the final document image quality and text readability.

The paper contributes in digital image binarization of text images suggesting a procedure based on image preparation, image type classification and thresholding and image refinement with applicability on Byzantine historical documents.

The purpose of this paper is to explore the proposition that corporate social responsibility reporting could be viewed as both an outcome of, and part of reputation risk management processes.

The paper draws heavily on management research. In addition, an image restoration framework is introduced.

The concept of reputation risk management could assist in the understanding of corporate social responsibility reporting practice.

This paper explores the link between reputation risk management and existing theorising in social accounting.

The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments.

The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information.

The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets.

Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.