Jan Klijn

Improved Nonlocal Means Based on Pre-Classification and Invariant Block Matching

One of the most popular image denoising methods based on self-similarity is called nonlocal means (NLM). Though it can achieve remarkable performance, this method has a few shortcomings, e.g., the computationally expensive calculation of the similarity measure, and the lack of reliable candidates for some nonrepetitive patches. In this paper, we propose to improve NLM by integrating Gaussian blur, clustering, and rotationally invariant block matching (RIBM) into the NLM framework. Experimental results show that the proposed technique can perform denoising better than the original NLM both quantitatively and visually, especially when the noise level is high.

Tone-mapping functions and multiple-exposure techniques for high dynamic-range images

For real-time imaging with digital video cameras and high-quality with TV display systems, good tonal rendition of video is important to ensure high visual comfort for the user. Except local contrast improvements, High Dynamic Range (HDR) scenes require adaptive gradation correction (tone-mapping function), which should enable good visualization of details at lower brightness. We discuss how to construct and control improved tone-mapping functions that enhance visibility of image details in the dark regions while not excessively compressing the image in the bright image parts. The result of this method is a 21-dB expansion of the dynamic range thanks to improved SNR by using multiple- exposure techniques. This new algorithm was successfully evaluated in HW and outperforms the existing algorithms with 11 dB. The new scheme can be successfully applied to cameras and TV systems to improve their contrast.