1.1 Motivation
With the rapid technological development, the visible light technology has become popular. Recently infrared sensing has also been widely applied in various domains of low/no light environments. One spectrum of infrared is near infrared (NIR), whose bandwidth is close to visible red light band, with a higher reflective image sensing capability under low/no light environments. In addition, NIR band also widely used in the military application providing high resolution images the sensing device of NIR is. It can be used as the enemy recognition systems and surveillance systems, in low/no light or fog or smoke in the environments.
It is well known that near infrared focal plane array (NIR FPA) has non-uniformity and bad pixels in the produced sensor cells. Hence, the infrared image must do non-uniformity correction (NUC) and bad pixel correction. Bad pixel is the pixel that does not respond (non responsive) i.e., dark situation (commonly) or always responsive i.e. In the NIR sensor bad pixel saturation is most often observed.
In the low light military applications, infrared image processing must be fast and efficient. Because military NIR sensor has to be lightweight and easy to used, there in NUC, the most popular reference-based correction method, so-called “two-point”
correction method in which two uniform sources of known intensity are sequentially imaged [1], [2] is widely used.
Bad pixel replacement of infrared focal plane arrays is also known as Dead Pixel Correction. Bad pixels are non-responsive, permanently dark or saturating. Bad pixels in infrared digital images might still remain uncorrected after non-uniformity
correction, so we need to correct bad pixels afterward. Because dead pixels in the infrared images are frequently in blobs than kinds of images, we must implement a specific method of image correction.
In this thesis, we use two-point correction to correct infrared images, non-uniformity and correct bad pixel. In the noise reduction scheme developed for better bad pixel detection, we propose a new impulse noise filter based on peer group concept. We employ adjustable window size that can increase the window automatically where bad pixels are in blobs. We use 3×3 window as default working window for sharpness maintenance, if the small window does not correct a bad pixel, the window size will increase automatically to enhance the correction capability. By this scheme, it is more accurate to locate bad pixel, and bad pixels will be replaced by the median of the peer group.
1.2 Non-uniformity Correction
With the development of infrared imaging technology, near infrared focal plane arrays (NIR FPA) imaging system has become the focus the next generation infrared imaging system. Compared with other thermal imaging systems, NIR FPA has simple structure, high reliability, high detection sensitivity and high frame rate, The NIR FPA is widely applied to various fields of military, medical, civil, and forest fire prevention.
Unfortunately, due to the limitations of semiconductor materials and process conditions, the output response of the detector is not the same, which resulted in the NIR FPA response non-uniformity. In general, the non-uniformity is called fixed pattern noise (FPN) will be striped or grid-like noise model. Therefore, how to
effectively track and remove the device non-uniformity, non-uniformity correction (NUC) is the key to improve the NIR FPA imaging quality.
There are several calibration methods for the NUC of an NIR FPA. In general, there are two categories of the calibration methods, reference-based and scene-based correction algorithm. Reference-Based (or calibration-based) NUC techniques are based on the use of uniform infrared sources. The most used one is the Two-Point Calibration method [3], which employs at least two blackbody sources at different luminance to calculate the gain and the offset of each detector on the NIR FPA.
Unfortunately, when the system is in use of increased working hours, its performance would be decreased for the working environment may change, Correction parameters which were measured before cannot meet the correct situation. Such kinds of Reference-Based NUC methods require to halt the operation of the system, and re-do the procedure and re-set the correction parameters to operate again.
For these reasons, Scene-Based NUC techniques are actually becoming more popular, since they only need the readout infrared data captured by the imaging system and compensate the non-uniform response of pixels during its normal operation. The constant statistics constraint method is the most referred scene-based technique However, its algorithm structure is complex, hardware implementation is difficult, thus reduces its engineering applications.
In this thesis, we propose to utilize two-point correction and adaptive scene-based NUC method [4] to correct infrared image that has non-uniformity. We also present varying-size impulse noise filter to correct bad pixels, and NIR sensor flowchart is illustrated in Fig. 1.1 below.
Fig 1.1 The flowchart of our NIR sensor.
1.3 Bad Pixel Correction
In many practical situations, the sensing devices and the transmission process tend to degrade the quality of the digital images by introducing noise, images are corrupted by the so-called impulsive noise of short duration and high energy. The presence of noise in an image may be a drawback in any subsequent processing to be done over the noisy image such as edge detection, image segmentation or pattern recognition. As a consequence, filtering the image to reduce the noise without degrading its quality, preserving edges, corners and other details is a major step in
imaging systems such as image content retrieval, medical image processing, industrial visual inspection [5]. This type of noise occurs mostly in the over-the-air transmission such as in standard broadcasting and satellite transmission. Common sources of impulse noise include lightening, industrial machines, car starters, faulty or dusty insulation of high-voltage powerlines and various unprotected electric switches [6–8].
In order to recovery the original image pixel values, the vector median filter (VMF) [9], which is probably the most well-known vector filter, uses the L1
(City-Block) or L2 (Euclidean) norm to define the above distance function. The filtering method sorts pixels vectors in the working window by space vector distance sum. On this basis, the Basic Vector Directional Filtering (BVDF) [10] sorts color vector by vector angle sum. Distance Directional Filtering (DDF) [11] sorts color vector by product of vector distance and vector angle. The above methods are too much smoothing, which results in an extensive blurring of the output image. This undesired property is caused by the unnecessary filtering of the noise-free samples that should be passed to a filter output without any change. To remove this drawback, a switching mechanism has been introduced into the structure of the robust smoothing filters, [12,13]. Such a switching filter detects if the pixel under consideration is affected by the noise process and if it is found to be noisy, then it is being replaced by the output of some robust filter, otherwise it is left unchanged. For example, Adaptive center-Weighted vector directional filter (ACWVDF) [14], and robust switching vector median filtering (RSVMF) [15]. When the noise ratio is low, the class switch-type methods have achieved good results.
In this thesis, we use improved peer group filter to correct infrared image that has non-uniformity and correct bad pixel.
1.4 Thesis Outline
The thesis is organized as follows. The basic concepts and technique concerning the NUC introduced in Chapter 2. The basic concepts and technique concerning the bad pixel correction are describled in Chapter 3. In Chapter 4, the results of our NIR methods which are introduced in Chapter 2 and Chapter 3 are shown and compared.
At last, we conclude this thesis with a discussion in Chapter 5.