Scene-based nonuniformity correction with video sequences and registration

We describe a new, to our knowledge, scene-based nonuniformity correction algorithm for array detectors. The algorithm relies on the ability to register a sequence of observed frames in the presence of the fixed-pattern noise caused by pixel-to-pixel nonuniformity. In low-to-moderate levels of nonuniformity, sufficiently accurate registration may be possible with standard scene-based registration techniques. If the registration is accurate, and motion exists between the frames, then groups of independent detectors can be identified that observe the same irradiance (or true scene value). These detector outputs are averaged to generate estimates of the true scene values. With these scene estimates, and the corresponding observed values through a given detector, a curve-fitting procedure is used to estimate the individual detector response parameters. These can then be used to correct for detector nonuniformity. The strength of the algorithm lies in its simplicity and low computational complexity. Experimental results, to illustrate the performance of the algorithm, include the use of visible-range imagery with simulated nonuniformity and infrared imagery with real nonuniformity

Sclerotinia rot of vegetables

SCLEROTINIA ROT, caused by the fungus Sclerotinia sclerotiorum, is now one of the most serious vegetable diseases in metropolitan market gardens. Over the past decade it has been steadily increasing in prevalence and if present trends continue, many growers may be forced to abandon certain crops. Although most vegetables are susceptible, the greatest losses are occurring in the autumn planting of beans, cauliflowers and lettuce and in the production of cauliflower seed

Plant diseases : Sclerotinia disease in vegetables : control with Allisan fungicide : a progress report

During the past decade sclerotinia rot has become a major disease problem in metropolitan market gardens. The fungicide Allisan has given promising results as a cover spray for the control of Sclerotinia. Two applications of the material reduced the incidence of Sclerotinia in runner beans from 45 per cent, to 15 per cent, and in lettuce from 9 per cent, to 2 per cent

Sclerotinia disease of vegetables : survival of the fungus in soil

Sclerotinia of vegetables is carried over in the soil of market gardens by means of resting fungal bodies called sclerotia. Recent tests have shown that sclerotia may stay alive in the sandy soils of Spearwood for two years, but disappear from the wet peat soils of Wanneroo within six month

Sclerotinia rot of beans

INVESTIGATIONS over the past three years suggest trenching is the most important practice for controlling Sclerotinia rot in market gardens. None of the remaining measures under test gave satisfactory control of the disease, although some reduced its incidence significantly

Accurate and robust image superresolution by neural processing of local image representations

Image superresolution involves the processing of an image sequence to generate a still image with higher resolution. Classical approaches, such as bayesian MAP methods, require iterative minimization procedures, with high computational costs. Recently, the authors proposed a method to tackle this problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we present a novel superresolution method, based on an evolution of this concept, to incorporate the use of local image models. A neural processing stage receives as input the value of model coefficients on local windows. The data dimension-ality is firstly reduced by application of PCA. An MLP, trained on synthetic se-quences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is exam-ined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method

Digital Image Processing

In recent years, digital images and digital image processing have become part of everyday life. This growth has been primarily fueled by advances in digital computers and the advent and growth of the Internet. Furthermore, commercially available digital cameras, scanners, and other equipment for acquiring, storing, and displaying digital imagery have become very inexpensive and increasingly powerful. An excellent treatment of digital images and digital image processing can be found in Ref. [1]. A digital image is simply a two-dimensional array of finite-precision numerical values called picture elements (or pixels). Thus a digital image is a spatially discrete (or discrete-space) signal. In visible grayscale images, for example, each pixel represents the intensity of a corresponding region in the scene. The grayscale values must be quantized into a finite precision format. Typical resolutions include 8 bit (256 gray levels), 12 bit (4096 gray levels), and 16 bit (65536 gray levels). Color visible images are most frequently represented by tristimulus values. These are the quantities of red, green, and blue light required, in the additive color system, to produce the desired color. Thus a so-called “RGB” color image can be thought of as a set of three “grayscale” images — the first representing the red component, the second the green, and the third the blue. Digital images can also be nonvisible in nature. This means that the physical quantity represented by the pixel values is something other than visible light intensity or color. These include radar cross-sections of an object, temperature profile (infrared imaging), X-ray images, gravitation field, etc. In general, any two-dimensional array information can be the basis for a digital image. As in the case of any digital data, the advantage of this representation is in the ability to manipulate the pixel values using a digital computer or digital hardware. This offers great power and flexibility. Furthermore, digital images can be stored and transmitted far more reliably than their analog counterparts. Error protection coding of digital imagery, for example, allows for virtually error-free transmission

A Collaborative Adaptive Wiener Filter for Image Restoration Using a Spatial-Domain Multi-patch Correlation Model

We present a new patch-based image restoration algorithm using an adaptive Wiener filter (AWF) with a novel spatial-domain multi-patch correlation model. The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF). The CAWF employs a finite size moving window. At each position, the current observation window represents the reference patch. We identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in the similar patches is used to estimate the center pixel in the reference patch. The weights are based on a new multi-patch correlation model that takes into account each pixel’s spatial distance to the center of its corresponding patch, as well as the intensity vector distances among the similar patches. One key advantage of the CAWF approach, compared with many other patch-based algorithms, is that it can jointly handle blur and noise. Furthermore, it can also readily treat spatially varying signal and noise statistics. To the best of our knowledge, this is the first multi-patch algorithm to use a single spatial-domain weighted sum of all pixels within multiple similar patches to form its estimate and the first to use a spatial-domain multi-patch correlation model to determine the weights. The experimental results presented show that the proposed method delivers high performance in image restoration in a variety of scenarios

A Collaborative Adaptive Wiener Filter for Multi-frame Super-resolution

Factors that can limit the effective resolution of an imaging system may include aliasing from under-sampling, blur from the optics and external factors, and sensor noise. Image restoration and super-resolution (SR) techniques can be used to improve image resolution. One SR method, developed recently, is the adaptive Wiener filter (AWF) SR algorithm. This is a multi-frame SR method that combines registered temporal frames through a joint nonuniform interpolation and restoration process to provide a high-resolution image estimate. Variations of this method have been demonstrated to be effective for multi-frame SR, as well demosaicing RGB and polarimetric imagery. While the AWF SR method effectively exploits subpixel shifts between temporal frames, it does not exploit self similarity within the observed imagery. However, very recently, the current authors have developed a multi-patch extension of the AWF method. This new method is referred to as a collaborative AWF (CAWF). The CAWF method employs a finite size moving window. At each position, we identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in all of the similar patches is used to estimate the center pixel in the reference patch. Like the AWF, the CAWF can perform nonuniform interpolation, deblurring, and denoising jointly. The big advantage of the CAWF, vs. the AWF, is the CAWF can also exploit self-similarity. This is particularly beneficial for treating low signal-to-noise ratio (SNR) imagery. To date, the CAWF has only been developed for Nyquist-sampled single-frame image restoration. In this paper, we extend the CAWF method for multi-frame SR. We provide a quantitative performance comparison between the CAWF SR and the AWF SR techniques using real and simulated data. We demonstrate that CAWF SR outperforms AWF SR, especially in low SNR applications