An improved pixon-based method is proposed in this paper for image segmentation. In this approach, a wavelet thresholding technique is initially applied on the image to reduce noise and to slightly smooth the image. This technique causes an image not to be oversegmented when the pixonbased method is used. Indeed, the wavelet thresholding, as a pre-processing step, eliminates the unnecessary details of the image and results in a fewer pixon number, faster performance and more robustness against unwanted environmental noises. The image is then considered as a pixonal model with a new structure. The obtained image is segmented using the hierarchical clustering method (Fuzzy C-Means algorithm). The experimental results in this paper indicate that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing image segmentation techniques.

Image segmentation is an essential and critical process in image processing and pattern recognition. In this paper, we proposed a textured-based method to segment an input image into regions. In our developed method, an entropy-based textured map of image is extracted, followed by a histogram equalization step to discriminate different regions. Then with the aim of eliminating unnecessary details and achieving more robustness against unwanted noises, a low-pass filtering technique is successfully used to smooth the image. As the next step, the appropriate pixons are extracted and delivered to a fuzzy c-mean clustering stage to obtain the final image segments. The results of applying the proposed method on several different images indicate its better performance in image segmentation compared to the other pixon-based methods, especially for the images with textured regions.

This paper proposes a new automatic image enhancement method via improving the image dynamic range. The improvement is performed via modifying the Gamma value of pixels in the image. Gamma distortion in an image is due to the technical limitations in the imaging device, and imposes a nonlinear effect. The severity of distortion in an image varies depending on the texture and depth of the objects. The proposed method locally estimates the Gamma values in an image. In this method, the image is initially segmented using a pixon-based approach. All of the pixels in each segment have similar characteristics in terms of the need for Gamma correction. Then, the Gamma value for each segment is estimated by minimizing the homogeneity of co-occurrence matrix. This feature can represent image details. The minimum value of this feature in a segment shows maximum details of the segment. The quality of an image is improved once more details are presented in the image via Gamma correction. In this study, it is shown that the proposed method performs well in improving the quality of images. Subjective and objective image quality assessments performed in this study attest the superiority of the proposed method compared to the existing methods in image quality enhancement.