Example-based image color and tone style enhancement

Color and tone adjustments are among the most frequent image enhancement operations. We define a color and tone style as a set of explicit or implicit rules governing color and tone adjustments. Our goal in this paper is to learn implicit color and tone adjustment rules from examples. That is, given a set of examples, each of which is a pair of corresponding images before and after adjustments, we would like to discover the underlying mathematical relationships optimally connecting the color and tone of corresponding pixels in all image pairs. We formally define tone and color adjustment rules as mappings, and propose to approximate complicated spatially varying nonlinear mappings in a piecewise manner. The reason behind this is that a very complicated mapping can still be locally approximated with a low-order polynomial model. Parameters within such low-order models are trained using data extracted from example image pairs. We successfully apply our framework in two scenarios, low-quality photo enhancement by transferring the style of a high-end camera, and photo enhancement using styles learned from photographers and designers. © 2011 ACM.postprin

Robust Automatic Focus Algorithm for Low Contrast Images Using a New Contrast Measure

Low contrast images, suffering from a lack of sharpness, are easily influenced by noise. As a result, many local false peaks may be generated in contrast measurements, making it difficult for the camera’s passive auto-focus system to perform its function of locating the focused peak. In this paper, a new passive auto-focus algorithm is proposed to address this problem. First, a noise reduction preprocessing is introduced to make our algorithm robust to both additive noise and multiplicative noise. Then, a new contrast measure is presented to bring in local false peaks, ensuring the presence of a well defined focused peak. In order to gauge the performance of our algorithm, a modified peak search algorithm is used in the experiments. The experimental results from an actual digital camera validate the effectiveness of our proposed algorithm

Benchmarking of mobile phone cameras

fi=vertaisarvioitu|en=peerReviewed

Interactive Image Processing for Electron Microscopy: Matching Hardware with Software

The image processing techniques used \u27a posteriori\u27 to extract information from electron micrographs are surveyed, including particularly image averaging, selective averaging, 3-D reconstruction, and high resolution focal series restoration; recent developments in online image pick up and control have led to fully automatic focussing, stigmating and alignment by a frame store system equipped with a real time correlator board. The diversity of the techniques encountered calls for large integrated program systems with flexible command languages; however, a dilemma exists between providing the user with convenient control of special hardware facilities such as frame stores and array processors, and preventing the programs from becoming so specific that they are extremely short lived. Some of the compromises made in the Semper system are noted