In this paper, a new denoising method for images degraded by white noise in blind condition using the Wiener filter has been proposed. The Wiener filter is a well-known denoising technique, but the performance comparison of the Wiener filter in different domains is not clear. This paper investigates and evaluates the Wiener filter implemented in the frequency, time and wavelet domains in an ideal case. It is shown that the frequency domain Wiener filter outperformes the Wiener filter implemented in the other domains. In addition, this paper proposes a power spectrum estimation method to be employed with the frequency domain Wiener filter. The performance is studied and found that the proposed method is capable to denoise noisy images in a wide range of noise levels and provides improved or equivalent performance relative to conventional methods.

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The technological advances made possible by the Internet, coupled with the unforeseen critical circumstances set in motion by the Covid-19 pandemic, have greatly increased the generation and transmission of medical images every day. Medical image transmission over an unsecured public network threatens the privacy of sensitive patient information. We have, in this paper, designed a new secure color medical image encryption algorithm based on binary plane decomposition, DNA (deoxyribonucleic acid) computing, and the chaotic Rössler dynamical system. At first, a bit-by-bit swap is performed on twenty four binary planes of the input image and encoded using DNA encoding rules. Thereafter, the Rössler system is used to modify the pixel values of the encoded image, which is subsequently decoded. Finally, the ciphered image is obtained by pixel-by-pixel permutation using position sequences. An innovative approach is used to compute keys from the color components of the input image. Extensive performance experiments of the proposed technique is conducted with metrics such as key sensitivity, key space, correlation coefficients (horizontal, diagonal and vertical directions), histograms, information entropy, number of pixel changes rate (NPCR), information entropy, unified average changing intensity (UACI), and encryption time. Comparative analyses have demonstrated that the proposed algorithm is fast, robust and competitive.

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Image enhancement provides human with better visual quality effects. However, images that are captured in low light condition are usually low-visibility and degraded by different noise types such as Poisson noise. Thus, this study goal is to develop an effective enhancement technique for low light condition wildlife observation digital image. This study proposed an effective enhancement technique which combines contrast enhancement technique and denoising technique. The proposed technique, HE_OWW filter utilizes Histogram Equalization and OTSU WIE-WATH filter to increase the visibility of low light image and remove Poisson noise found in wildlife observation image due to low light intensities. The performance of proposed method is evaluated through objective and subjective measure. For objective measure, the proposed method is analyzed by Root Mean Square (RMS) Contrast, luminance, Perceptual Sharpness Index (PSI) and computational time. Meanwhile, subjective measure is done by visual effects inspection. Proposed method provided the best result in limited light source image with RMS Contrast of 0.6004, luminance of 0.4354 and PSI of 0.4492 and took the shortest time to compute the test images. The result shows that proposed method is the best among compared techniques for enhancement of wildlife observation image with low light source.