Image restoration in digital photography

This paper introduces some novel image restoration algorithms for digital photography, which has one of the fastest growing consumer electronics markets in recent years. Many attempts have been made to improve the quality of the digital pictures in comparison with photography taken on films. A lot of these methods have their roots in discrete signal and image processing developed over the last two decades, but the ever-increasing computational power of personal computers has made possible new designs and advanced techniques. The algorithms we are presenting here take advantage of the programmability of the pixels and the availability of a compression codec commonly found inside digital cameras, and work in compliance with either the JPEG or the JPEG-2000 image compression standard.published_or_final_versio

Optical Signal Processing: Poisson Image Restoration and Shearing Interferometry

Optical signal processing can be performed in either digital or analog systems. Digital computers and coherent optical systems are discussed as they are used in optical signal processing. Topics include: image restoration; phase-object visualization; image contrast reversal; optical computation; image multiplexing; and fabrication of spatial filters. Digital optical data processing deals with restoration of images degraded by signal-dependent noise. When the input data of an image restoration system are the numbers of photoelectrons received from various areas of a photosensitive surface, the data are Poisson distributed with mean values proportional to the illuminance of the incoherently radiating object and background light. Optical signal processing using coherent optical systems is also discussed. Following a brief review of the pertinent details of Ronchi's diffraction grating interferometer, moire effect, carrier-frequency photography, and achromatic holography, two new shearing interferometers based on them are presented. Both interferometers can produce variable shear

A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging

Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme.Comment: Some figures are reduced to comply with arxiv's size constraints. Full size images are available as HAL technical report hal-01107519v5, IEEE Transactions on Computational Imaging, 201

Digital non-metric image-based documentation for the preservation and restoration of mural paintings: the case of the Üzümlü Rock-hewn Church, Turkey

[EN] Digital photography is a valuable documentation technique for the preservation of a cultural heritage site because highresolution photography presents both general and detailed views of mural paintings and mural condition in a single image. Advanced digital technology is particularly helpful for preserving and restoring mural paintings given that the painting condition is recorded on high-resolution base maps shows how mural paintings are damaged by environmental stresses, mechanical damages and inappropriate treatments, among others. In addition, photogrammetric software technology is rapidly advancing and being applied to the digital documentation of mural paintings or rock art. Nevertheless, human experience and investigation of mural paintings is indispensable for recording the condition of mural paintings, and this highlights that every step of documentation conducted in situ is desirable. However, images by photogrammetric software do not show sufficient resolution because most normal portable computers used on-site are not usually sufficient. Based on our experience at the Üzümlü Church in Cappadocia, Turkey, we propose a new approach to document mural conditions in situ for preservation and restoration. Our method is based on a comparison of a non-metric but approximate high-resolution image with the actual mural paintings. The method does not require special instruments and enables digital documentation of the mural condition in situ at a low cost, in a short time frame and using minimal human resources.This work was supported by the JSPS KAKENHI Grantin-Aid for young Scientists (B) under Grant Number 24760528; and the JSPS KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas under Grant Number 24101014.Higuchi, R.; Suzuki, T.; Shibata, M.; Taniguchi, Y.; Gülyaz, M. (2016). Digital non-metric image-based documentation for the preservation and restoration of mural paintings: the case of the Üzümlü Rock-hewn Church, Turkey. Virtual Archaeology Review. 7(14):31-42. https://doi.org/10.4995/var.2016.4241SWORD314271

Remote sensing of tidal networks and their relation to vegetation

The study of the morphology of tidal networks and their relation to salt marsh vegetation is currently an active area of research, and a number of theories have been developed which require validation using extensive observations. Conventional methods of measuring networks and associated vegetation can be cumbersome and subjective. Recent advances in remote sensing techniques mean that these can now often reduce measurement effort whilst at the same time increasing measurement scale. The status of remote sensing of tidal networks and their relation to vegetation is reviewed. The measurement of network planforms and their associated variables is possible to sufficient resolution using digital aerial photography and airborne scanning laser altimetry (LiDAR), with LiDAR also being able to measure channel depths. A multi-level knowledge-based technique is described to extract networks from LiDAR in a semi-automated fashion. This allows objective and detailed geomorphological information on networks to be obtained over large areas of the inter-tidal zone. It is illustrated using LIDAR data of the River Ems, Germany, the Venice lagoon, and Carnforth Marsh, Morecambe Bay, UK. Examples of geomorphological variables of networks extracted from LiDAR data are given. Associated marsh vegetation can be classified into its component species using airborne hyperspectral and satellite multispectral data. Other potential applications of remote sensing for network studies include determining spatial relationships between networks and vegetation, measuring marsh platform vegetation roughness, in-channel velocities and sediment processes, studying salt pans, and for marsh restoration schemes

Light field super resolution through controlled micro-shifts of light field sensor

Light field cameras enable new capabilities, such as post-capture refocusing and aperture control, through capturing directional and spatial distribution of light rays in space. Micro-lens array based light field camera design is often preferred due to its light transmission efficiency, cost-effectiveness and compactness. One drawback of the micro-lens array based light field cameras is low spatial resolution due to the fact that a single sensor is shared to capture both spatial and angular information. To address the low spatial resolution issue, we present a light field imaging approach, where multiple light fields are captured and fused to improve the spatial resolution. For each capture, the light field sensor is shifted by a pre-determined fraction of a micro-lens size using an XY translation stage for optimal performance